\pdfbookmark[0]{Figures}{figures}

```{=latex}
\ThreePanelFigCustomWidth
%%%%% Full Figure Config %%%%%
    {spending_changes} % label
    {Changes in Consumer Spending During the COVID Pandemic} % title
%%%%% Panel A %%%%%
    {../results/Spending/Spending Changes by Income Quartile} % filepath
    {spending_changes_by_income_quartile} % label
    {Spending Changes by Income Quartile} % title
    {0.8\textwidth} % width
%%%%% Panel B %%%%%
    {../results/Spending/Spending Changes by Industry Stacked Bar} % filepath
    {spending_changes_by_sector} % label
    {Spending Changes by Sector} % title
    {0.495\textwidth} % width
%%%%% Panel C %%%%%
    {../results/Spending/Spending Changes by Sector COVID vs GFC} % filepath
    {spending_changes_by_sector_covid_gfc} % label
    {Spending Changes by Sector: COVID vs Great Recession} % title
    {0.495\textwidth} % width
%%%%% Notes %%%%%
    {This figure disaggregates spending changes by income and sector using debit and credit card data from Affinity Solutions and national accounts (NIPA) data. Panel A plots daily spending levels for consumers in the highest and lowest quartiles of household income by combining total card spending in January 2020 (from NIPA Table 2.3.5) with our Affinity Solutions spending series.  See the notes to Appendix Table \ref{tab:spending_changes} for details on this method. Panel B disaggregates the sectoral shares of seasonally-adjusted spending changes (left bar) and pre-COVID spending levels (right bar). See Appendix \ref{subsec:Data-Affinity-Masking-and-Publication} for the definitions of the sectors plotted in Panel B. Panel C decomposes the change in personal consumption expenditures (PCE) in the Great Recession and the COVID-19 Recession using NIPA Table 2.3.6. PCE is defined here as the sum of durable goods, non-durable goods and services in seasonally adjusted, chained (2012) dollars. The peak to trough declines are calculated from December 2007 to June 2009 for the Great Recession and from January to April 2020 for the COVID-19 Recession. Data sources: Affinity Solutions, NIPA.}
    {}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {covid_isolation_associations} % label
    {Association Between COVID-19 Incidence and Changes in Consumer Spending} % title
    {../results/COVID-19 Incidence/Spending changes vs COVID case rate} % filepath
%%%%% Notes %%%%%
    {This figure presents a county-level binned scatter plot. To construct it, we divide the data into twenty equal-sized bins, ranking by the x-axis variable and weighting by the county’s population, and plot the (population-weighted) means of the y-axis and x-axis variables within each bin. The y-axis presents the change in seasonally adjusted consumer spending from the base period (January 6 to February 2, 2020) to the three-week period of March 25 to April 14, 2020 (see Section \ref{subsec:Data-Spending} and Appendix \ref{sec:Appx-Affinity} for details on the construction of our consumer spending series). The x-axis variable is the logarithm of the county's cumulative COVID case rate per capita as of April 14, 2020; with axis labels showing the levels on a logarithmic scale. We  plot values separately for counties in the top and bottom quartiles of median household income (measured using population-weighted 2014-2018 ACS data). Data sources: Affinity Solutions, New York Times.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {smallbiz_zip_associations} % label
    {Changes in Small Business Revenues vs. Median Two Bedroom Rent, by ZIP} % title
    {../results/Small Business Revenue/Small Business Revenue vs Median Rent binscatter} % filepath
%%%%% Notes %%%%%
    {This figure presents a binned scatter plot showing the relationship between changes in seasonally-adjusted small business revenue in Womply data vs. rent at the ZIP code level. The binned scatter plot is constructed as described in Figure \ref{fig:covid_isolation_associations}. We measure changes in small business revenue as the average value of our index at the ZIP code level between March 23 and April 12, 2020 (see Section \ref{subsec:Data-Revenue} and Appendix \ref{sec:Data-Womply} for details on the construction of our small business revenue series). The x-axis variable is the ZIP code median rent for a two-bedroom apartment in the 2014-2018 ACS. Data sources: Womply, ACS.}
```

```{=latex}
\threepanelfig
%%%%% Full Figure Config %%%%%
    {employment_jobs_vs_rent} % label
    {Changes in Job Postings and Employment Rates vs. Rent} % title   
%%%%% Panel A %%%%%
    {../results/Jobs/Job postings for Low-Education Workers vs Median Rent Binscatter} % filepath
    {jobs_loweduc_vs_rent} % label
    {Job Postings for Low-Education Workers vs. Median Rent, by County \\ \vspace{3mm} April 2020} % title
%%%%% Panel B %%%%%
    {../results/Jobs/Job postings for High-Education Workers vs Median Rent Binscatter} % filepath
    {jobs_higheduc_vs_rent} % label
    {Job Postings for High-Education Workers vs. Median Rent, by County \\ \vspace{3mm} April 2020} % title
%%%%% Panel C %%%%%
    {../results/Employment/Low-wage Employment vs Median Rent by County Binscatter} % filepath
    {employment_vs_rent} % label
    {Low-Wage Employment vs. Median Rent, by County \\ \vspace{3mm} July 2020} % title   
%%%%% Notes %%%%%
    {This figure shows binned scatter plots of the relationship between median rents and changes in job postings (Panels A and B) or changes in employment rates (Panel C). The binned scatter plots are constructed as described in Figure \ref{fig:covid_isolation_associations}. Solid lines are best-fit lines estimated using OLS. Each panel also displays the slope coefficient and standard error of the corresponding linear OLS regression. In each panel, the x-axis variable is the median rent within a county for a two-bedroom apartment in the 2014-2018 ACS. In Panel A, the y-axis variable is the average value of our job postings series for jobs requiring minimal or some education between March 25 and April 14, 2020 (see Section \ref{subsec:Data-Jobs} and Appendix \ref{sec:Data-Burning-Glass} for more detail on our job postings series). Panel B replicates Panel A with job postings for workers with moderate, considerable, or extensive education. In both Panels A and B, we winsorize our job postings series at the 99th percentile of the (population-weighted) county level distribution within each level of required education. In Panel C, the y-axis variable is the average value of our bottom wage quartile employment series during the month of July 2020 (see Section \ref{subsec:Data-Employment} and Appendix \ref{sec:Data-Employment} for more detail on the construction of our employment series). Data sources: Paychex, Intuit, Lightcast, ACS.}
```

```{=latex}
\twopanelfig
%%%%% Full Figure Config %%%%%
    {employment_changes} % label
    {Changes in Employment by Wage Quartile} % title
%%%%% Panel A %%%%%
    {../results/Employment/Changes in Employment by Income Quartile - long} % filepath
    {employment_changes_by_income_quartile} % label
    {Changes in Employment by Wage Quartile} % title
%%%%% Panel B %%%%%
    {../results/Employment/Changes in Employment by Income Quartile Reweighting - long} % filepath
    {employment_changes_reweighted} % label
    {Changes in Employment by Wage Quartile, Reweighting Across Industries and Areas} % title
%%%%% Notes %%%%%
    {Panel A plots our combined Paychex-Intuit employment series from January 2020 through December 2021 for each wage quartile.  We define moving wage quartiles thresholds in each month based on 100\%, 150\% and 250\% of the federal poverty line (FPL) for a family of four, adjusted for inflation, then converted into a full-time-equivalent hourly wage by dividing by 2,000 hours (50 weeks of work at 40 hours per week). In January 2020, the thresholds were \${{wage_threshold_q1q2}}, \${{wage_threshold_q2q3}} and \${{wage_threshold_q3q4}} and the four bins in ascending order by wage contained {{pct_emp_cps_q1}}\%, {{pct_emp_cps_q2}}\%, {{pct_emp_cps_q3}}\%, and {{pct_emp_cps_q4}}\% of CPS respondents. See Section \ref{subsec:Data-Employment} and Appendix \ref{sec:Data-Employment} for details on the construction of this series. In Panel B, we reweight the county-by-industry (2-digit NAICS) distribution of bottom wage quartile employment to match the distribution for top wage quartile employment in January 2020. For each series in Panel B, we restrict the sample to county-by-industry cells with non-zero employment in all four wage quartiles in January 2020; this sample restriction excludes 2.5\% of worker-days from the sample. Data sources: Paychex, Intuit.}
    {}
```

```{=latex}
\fourpanelfig
%%%%% Full Figure Config %%%%%
    {slopes_over_time} % label
    {Evolution of the Association between Low-Education Job Postings and Low-Wage Employment with Rent} % title
	%%%%% Panel A %%%%%
    {../results/Jobs/Low-Educ Job Postings vs Rent - December 2021} % filepath
    {jobs_rent_dec2021} % label
    {Job Postings for Low-Education Workers vs. Median Rent, by County \\ \vspace{3mm} December 2021} % title
	%%%%% Panel B %%%%%
    {../results/Employment/Employment vs Rent - December 2021} % filepath
    {emp_rent_dec2021} % label
    {Low-Wage Employment vs. Median Rent, by County \\ \vspace{3mm} December 2021} % title
%%%%% Panel C %%%%%
    {../results/Jobs/Jobs - Evolution of rent gradient - Low-Educ} % filepath
    {slopes_over_time_jobposts_rent} % label
    {Relationship Between Low-Education Job Postings and Rent, by County} % title
%%%%% Panel D %%%%%
    {../results/Employment/Employment - Evolution of rent gradient - Q1 Employment} % filepath
    {slopes_over_time_employment_rent} % label
    {Relationship Between Low-Wage Employment and Rent, by County} % title
%%%%% Notes %%%%%
    {This figure presents a summary of the results of a set of regressions documenting the relationship between job postings and employment with rent over time. Panel A replicates Figure \ref{fig:jobs_loweduc_vs_rent}, but using the average value of the low-education job postings series in December 2021 instead of April 2020. Panel B replicates Figure \ref{fig:employment_vs_rent}, but using the average value of the Paychex-Intuit employment series in December 2021 instead of July 2020. The binned scatter plots are constructed as described in Figure \ref{fig:covid_isolation_associations}. Panel C plots the slope of the best-fit line from a population-weighted regression of low-education job postings on median county rent (as in Panel A) for each month from April 2020 through December 2021. The slopes estimated in Figures \ref{fig:jobs_loweduc_vs_rent} and \ref{fig:jobs_rent_dec2021} are the first and last estimates in this series, respectively. Panel D replicates Panel C for the slope of the bottom wage quartile employment vs. median rent (as in Panel B). In both Panels C and D, the dashed lines above and below the solid series represent the upper and lower boundaries of the 95\% confidence interval for the slope estimated in each month. Panels B and D omit counties from CA, MA, and NY, since these three states raised the minimum wage at some point after July 2020 above our upper threshold for the bottom wage quartile of employment. Data sources: Lightcast, Paychex, Intuit, ACS.}
```

```{=latex}
\fourpanelfig
%%%%% Full Figure Config %%%%%
    {stimulus_eventstudy} % label
    {Effects of Stimulus Payments on Spending: Event Studies} % title
%%%%% Panel A %%%%%
    {../results/Policy/Stimulus scatterplot for April round window Q1} % filepath
    {stimulus1_eventstudy_lowincome} % label
    {Stimulus 1: Bottom Income Quartile} % title
%%%%% Panel B %%%%%
    {../results/Policy/Stimulus scatterplot for April round window Q4} % filepath
    {stimulus1_eventstudy_highincome} % label
    {Stimulus 1: Top Income Quartile} % title
%%%%% Panel C %%%%%
    {../results/Policy/Stimulus scatterplot for Jan round Q1 and 4} % filepath
    {stimulus2_eventstudy} % label
    {Stimulus 2: Bottom vs. Top Income Quartile} % title
%%%%% Panel D %%%%%
    {../results/Policy/Stimulus scatterplot for March round Q1 and 4 pre line} % filepath
    {stimulus3_eventstudy} % label
    {Stimulus 3: Bottom vs. Top Income Quartile} % title
%%%%% Notes %%%%%
    {This figure shows event studies of the effect of stimulus payments on consumer spending. We measure consumer spending using data from Affinity Solutions. To construct each consumer spending time series, we express consumer spending on each day as a percentage change relative to mean daily consumer spending over January 2019, residualize these daily percentage changes with respect to day-of-week fixed effects (estimated out-of-sample using data in 2019), calculate the first difference with respect to values from the corresponding period starting in 2019, and adjust the estimates for a linear pre-trend in first differences. Panel A depicts this spending time series for 25 days before and after April 15, 2020 (the modal date for deposits of the CARES Act economic impact payments) for cardholders with residential addresses in the bottom income quartile of ZIP codes. We exclude April 14, 2020 from the pre-period as some households received stimulus payments on this date. Panel B repeats this figure for the top income quartile of ZIP codes. Panel C repeats Panels A and B for the days around January 4, 2021 (the modal date for deposits of the COVID-Related Tax Relief Act economic impact payments), plotting outcomes for both the bottom and top income quartiles. The pre-period in Panel C runs from December 4 to 14, 2020, with the holiday period (December 15, 2020 to January 3, 2021) removed due to high daily volatility in spending levels (see Section \ref{subsec:Stimulus-Eval} and Appendix Figure \ref{fig:variance_week} for more details). The post-period runs from January 4 to 19, 2021, reflecting the data available when this analysis was originally published on January 26, 2021. Due to the omission of the holiday period, we do not remove a linear pre-trend as in Panels A and B. Panel D repeats Panel C for the days around March 17, 2021 (the modal date for deposits of the American Rescue Plan Act economic impact payments). We exclude March 13 to 16, 2021 from the pre-period as payments were made starting March 13. In Panels A, B, and D, we interpolate the value for Easter Sunday using the average of adjacent daily values. Data sources: Affinity Solutions, ACS.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {stimulus_effect_sizes} % label
    {Impacts of Stimulus Payments on Spending, by Income Quartile} % title
    {../results/Policy/Stimulus bar chart by round and income quartile (dollars)} % filepath
%%%%% Notes %%%%%
    {This figure plots estimates of the marginal propensity to spend out of stimulus payments in the first month after receipt for each of the three rounds of stimulus payments, separately by income quartile (based on median ZIP code income). The estimates are scaled per \$1,200 of stimulus payment and correspond to the “Combined Dollar” estimates reported in Column 5 of Appendix Table \ref{tab:policy_stimulus}. See Section \ref{subsec:Stimulus-Eval} and Appendix \ref{subsec:Stimulus-Policy-Rescaling} for details on how these estimates were calculated. We also report p-values testing the null hypothesis of equal effect sizes between each pair of stimulus rounds, for the highest- and lowest-quartile of ZIP-level incomes. These p-values are based on permutation tests reported in Appendix Figures \ref{fig:permutations_q1} and \ref{fig:permutations_q4}. Data source: Affinity Solutions.}
```

```{=latex}
\fourpanelfig
%%%%% Full Figure Config %%%%%
    {employment_spending_vs_workplacerent} % label
    {Changes in Employment and Consumer Spending for Low-Income Households vs. Workplace Rent} % title
%%%%% Panel A %%%%%
    {../results/Employment/Change in Low-Income Employment vs. Workplace Rent} % filepath
    {employment_vs_workplacerent_apr2020} % label
    {Low-Wage Employment vs. Workplace Rent, by ZIP \\ \vspace{3mm} April 2020} % title
%%%%% Panel B %%%%%
    {../results/Spending/Change in Consumer Spending vs Workplace Rent - April 2020} % filepath
    {spending_vs_workplacerent_apr2020} % label
    {Spending Among Low-Income Households vs. Workplace Rent, by ZIP \\ \vspace{3mm} April 2020} % title
%%%%% Panel B %%%%%
    {../results/Spending/Change in Consumer Spending vs Workplace Rent - October 2020} % filepath
    {spending_vs_workplacerent_oct2020} % label
    {Spending Among Low-Income Households vs. Workplace Rent, by ZIP \\ \vspace{3mm} October 2020} % title
%%%%% Panel D %%%%%
    {../results/Spending/Spending of Low-Income Households in High Rent ZIPs} % filepath
    {spending_rent_slope_evol} % label
    {Spending by Low-Income Households Living in ZIP Codes with High Workplace Rent \\ \vspace{7mm}} % title
%%%%% Notes %%%%%
    {This figure examines the relationship between low-wage employment and consumer spending for individuals living in a home ZIP code $z$ with the average rent in the ZIP codes of the workplaces for low-wage workers who live in home ZIP code $z$. Panel A presents a binned scatterplot showing the relationship between low-wage employment for workers living in a home ZIP code and the average median rent in the workplace ZIP codes for low-wage workers from that home ZIP code. We measure low-wage employment in each home ZIP code using the Earnin employment series in April 2020. We then match each home ZIP code to the distribution of workplace ZIP codes using the Census' LODES data for low-wage workers. We calculate the x-axis variable as the average median rent for a two-bedroom apartment (measured in the 2014-2018 ACS), averaged across workplace ZIP codes using the distribution from the LODES data for each home ZIP code. See Section \ref{subsec:Secondary-Spending-Impacts} for a detailed discussion. Panel B replicates Panel A for a different outcome: average consumer spending between March 25 and April 14, 2020, restricting to ZIP codes in the bottom quartile of median income, as measured in the 2014-2018 ACS. Panel C replicates Panel B with consumer spending instead measured during the month of October 2020. The binned scatter plots are constructed as described in Figure \ref{fig:covid_isolation_associations}. Panel D plots the average level of consumer spending for the top quartile of households appearing in Panels B and C ranked on average median workplace rent (i.e., the five right-most dots) in each month from February 2020 through December 2021. Data sources: Earnin, Affinity Solutions, Census LODES, ACS.}
```

```{=latex}
\onepanelfig
%%%%% Full Figure Config %%%%%
    {education_by_income_quartile} % label
    {Effects of COVID-19 on Educational Progress by Income Group} % title
    {../results/Education/Education - Educational Progress by income} % filepath
%%%%% Notes %%%%%
    {This figure plots a time series of student engagement on the Zearn Math online platform, splitting schools into quartiles based on the share of students in the school eligible for Free or Reduced Price Lunch (FRPL). We measure student engagement as the average number of students using the Zearn Math application in each week, relative to the mean value of students using the platform in the same classroom during the reference period  of January 6 to February 7, 2020. We restrict the sample to classrooms with at least 10 students using Zearn on average and at least 5 students doing so in each week during the reference period. We measure the share of students eligible for Free and Reduced Price Lunch in each school using demographic data from the Common Core dataset from MDR Education, a private education data firm. Data sources: Zearn, Common Core.}
```